Spatially weighted supervised classification for remote sensing

A simple approach for incorporating a spatial weighting into a supervised classifier for remote sensing applications is presented. The classifier modifies the feature-space distance-based metric with a spatial weighting. This is facilitated by the use of a non-parametric (k-nearest neighbour, k-NN) classifier in which the spatial location of each pixel in the training data set is known and available for analysis. A remotely sensed image was simulated using a combined Boolean and geostatistical unconditional simulation approach. This simulated image comprised four wavebands and represented three classes: Managed Grassland, Woodland and Rough Grassland. This image was then used to evaluate the spatially weighted classifier. The latter resulted in modest increase in the accuracy of classification over the original k-NN approach. Two spatial distance metrics were evaluated: the non-centred covariance and a simple inverse distance weighting. The inverse distance weighting resulted in the greatest increase in accuracy in this case.     

后遥感应用技术研究与地质实践——以东胜—神木地区铀资源勘查为例

介绍了后遥感应用技术提出的背景及技术构成,阐述了该技术的研究内容及取得的地质成果。实践表明,在地质勘查领域,后遥感应用技术比单一遥感技术具有更大的实用价值和更好的应用效果。     

Accuracy analysis, dem generation and validation using russian tk-350 stereo-images

TK-350 stereo-scenes of the Zonguldak testfield in the north-west of Turkey have been analysed. The imagery had a base-to-height ratio of 0·52 and covered an area of 200 km × 300 km, with each pixel representing 10 m on the ground. Control points digitised from 1:25 000 scale topographic maps were used in the test. A bundle orientation was executed using the University of Hanover program BLUH and PCI Geomatica OrthoEngine AE software packages. Tests revealed that TK-350 stereo-images can yield 3D geopositioning to an accuracy of about 10 m in planimetry and 17 m in height. A 40 m resolution digital elevation model (DEM) was generated by the PCI system and compared against a reference DEM, which was derived from digitised contour lines provided by 1:25 000 scale topographic maps. This comparison showed that accuracy depends mainly on the surface structure and the slope of the local terrain. Root mean square errors in height were found to be about 27 and 39 m outside and inside forested areas, respectively. The matched DEM demonstrated a systematic shift against the reference DEM visible as an asymmetric shift in the frequency distribution. This is perhaps caused by the presence of vegetation and buildings.     

基于遥感信息的土地资源可持续利用研究

遥感信息是对自然界的反映，它具有宏观、动态、准确、快速的优点，可以及时提供动态数据。基于遥感信息，对吉林西部土地资源利用状况进行调查，运用主成分分析法，可以不损失或很少损失原有的信息，将原来个数较多而且彼此相关的变量转换为新的个数较少而且彼此独立或不相关的新变量。对土地资源可持续利用进行定量分析研究，指出土地利用中存在的问题，并提出吉林西部土地资源可持续利用发展模式。     

Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review

The conventional means to record hydrological parameters of aflood often fail to record an extreme event. Remote sensingtechnology along with geographic information system (GIS)has become the key tool for flood monitoring in recent years.Development in this field has evolved from optical to radarremote sensing, which has provided all weather capabilitycompared to the optical sensors for the purpose of flood mapping.The central focus in this field revolves around delineation of floodzones and preparation of flood hazard maps for the vulnerable areas.In this exercise flood depth is considered crucial for flood hazardmapping and a digital elevation model (DEM) is considered to bethe most effective means to estimate flood depth from remotelysensed or hydrological data. In a flat terrain accuracy of floodestimation depends primarily on the resolution of the DEM. Riverflooding in the developing countries of monsoon Asia is very acutebecause of their heavy dependence on agriculture but any floodestimation or hazard mapping attempt in this region is handicappedby poor availability of high resolution DEMs. This paper presents areview of application of remote sensing and GIS in flood managementwith particular focus on the developing countries of Asia.     

利用遥感综合分析西风引导气流与地形对沙尘运移路径的影响

沙尘暴的发生受大气环流、地表状况、降雨的影响，还受到局部地区地形的影响。一次规模较大的沙尘暴过程，沙尘可以从蒙古国和我国西部沙源地输送到我国东部、韩国、日本乃至夏威夷、美国西海岸。中日亚洲沙尘暴ADEC项目对亚洲沙尘暴的起沙、传输和降落的运行机制已经作了深入的研究，并建立了亚洲沙尘暴的数值模拟系统。本文以影响北京地区的沙尘暴事件为例，利用遥感技术，综合DEM地形数据和地面实测数据分析西风引导气流和地形对沙尘运移路径影响，将MODIs影像数据和DEM地形数据以及地面观测站点实测数据相结合，进行综合分析，结果表明在一次沙尘暴过程中，沙尘在运移过程中的运移路径明显地受到西风引导气流、沙尘粒子自然沉降规律以及局部地形的影响，要预防(减少)北京地区的沙尘暴仅仅作好北京地区的生态环境建设是不够的，加强北京周边地区，尤其是张家口地区、官厅水库库区及库区周围地区的生态环境建设尤为重要。     

2002年秋季山东省干旱遥感监测分析

利用极轨气象卫星遥感资料对2002年山东省秋季干旱进行监测和服务,其监测方法主要采用热惯量法。为了提高土壤水分模式的计算精度,将全省分为4个区域,对利用卫星资料反演的地表温度日较差进行植被指数等环境因素订正。在地理信息系统支持下,比较准确地计算出农田受旱面积,效果良好。     

基于PRS的遥感图像地物信息提取模型

点源理论(PRS)是一种面向应用需求的集中信息处理方式。不同尺度、不同分辨率的点都是一定的综合信息场,是客观世界的真实反映。本文在数字图像处理过程中引入点源理论,探讨针对地物空间信息应用服务的遥感信息分析和处理模型,讨论基于点源的图像构造、知识构造及地物模型建造过程,给出基于点源的遥感图像地物提取流程。     

概率神经网络与BP网络模型在遥感图像分类中的对比研究

通过分析概率神经网络(以下称PNN)的基本结构及其训练算法,建立了卫星图像分类的概率神经网络模型,并通过实例对比分析了概率神经网络与BP网络分类模型的分类效果。实验表明,PNN图像分类方法在分类精度上优于误差反向传播神经网络模型,且分类时间相当,是一种有效的图像分类方法。     

基于灰度形态学的高分辨率遥感影像预处理

提出了一种从高分辨率遥感影像中提取线性目标时影像预处理的方法。该方法基于灰度形态学的Top-Hat运算和灰度膨胀运算，用Top-Hat算子除去影像的噪声，再利用灰度膨胀增强影像目标和背景之间的对比度，使目标更易于识别。经过处理的遥感影像，能够更有效地提取影像的目标。